Electronic tongues to assess wine sensory descriptors

From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry

Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography

A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.

Multi-Sensor Characterization of Sparkling Wines Based on Data Fusion

This paper is focused on the assessment of a multi-sensor approach to improve the overall characterization of sparkling wines (cava wines). Multi-sensor, low-level data fusion can provide more comprehensive and more accurate vision of results compared with the study of simpler data sets from individual techniques. Data from different instrumental platforms were combined in an enriched matrix, integrating information from spectroscopic (UV/Vis and FTIR), chromatographic, and other techniques. Sparkling wines belonging to different classes, which differed in the grape varieties, coupages, and wine-making processes, were analyzed to determine organic acids (e.g., tartaric, lactic, malic, and acetic acids), pH, total acidity, polyphenols, total antioxidant capacity, ethanol, or reducing sugars. The resulting compositional values were treated chemometrically for a more efficient recovery of the underlaying information. In this regard, exploratory methods such as principal component analysis showed that phenolic compounds were dependent on varietal and blending issues while organic acids were more affected by fermentation features. The analysis of the multi-sensor data set provided a more comprehensive description of cavas according to grape classes, blends, and vinification processes. Hierarchical Cluster Analysis (HCA) allowed specific groups of samples to be distinguished, featuring malolactic fermentation and the chardonnay and red grape classes. Partial Least Squares-Discriminant Analysis (PLS-DA) also classified samples according to the type of grape varieties and fermentations. Bar charts and complementary statistic test were performed to better define the differences among the studied samples based on the most significant markers of each cava wine type. As a conclusion, catechin, gallic, gentisic, caftaric, caffeic, malic, and lactic acids were the most remarkable descriptors that contributed to their discrimination based on varietal, blending, and oenological factors.

Four-channel fluorescent sensor array based on various functionalized CdTe quantum dots for the discrimination of Chinese baijiu

As a special carrier of traditional Chinese culture, baijiu is rich in terms of types and ingredients. Its quality analysis and control are always important and complex issues that urgently need reliable evaluation methods. In this study, four different modified CdTe quantum dots (QDs) were used to characterize their sensing performance to various baijiu. A sensor array was then constructed through the complementary properties of differential fluorescence signals. To achieve an accurate and rapid evaluation of different baijiu types, a linear discriminant analysis (LDA) was introduced to extract and process spectral information. And the array was able to distinguish commercial baijiu samples with different aroma-types, brands, qualities and storage years with a recognition rate of 100%. In addition, according to the heat map, the organic acids in baijiu were shown to be the main components causing the fluorescence change through electron transfer (hydrogen bond) and resonance energy transfer among QDs and acids. Furthermore, using the partial least squares regression (PLSR) model, five representative organic acids were accurately quantified with a quantitative range of 10 μmol/L-80 μmol/L with a high selectivity. This QDs fluorescence sensing strategy provides an accurate, simple, and fast baijiu sensing method, which provides a potential use for on-line baijiu monitoring.

Developing an authentication approach using SPME-GC-IRMS based on compound-specific δ 13C analysis of six typical volatiles in wine

An analytical method using gas chromatography isotope ratio mass spectrometry (GC-IRMS) combined with solid phase micro-extraction (SPME) was developed to measure the δ 13C values of six typical volatiles commonly occurring in wine (isoamyl acetate, 2-octanone, limonene, 2-phenylethanol, ethyl octanoate and ethyl decanoate) for the first time. SPME selected with a divinylbenzene/carboxen/polydimethylsiloxane fiber was combined with the GC-IRMS for pretreatment optimization. The optimized SPME parameters of extraction time, extraction temperature and salt concentration were 40 min, 40 °C and 10%, respectively. The δ 13C values measured by SPME-GC-IRMS were in good agreement with those measured via elemental analyzer (EA)-IRMS and GC-IRMS. The differences range from 0.02 to 0.44‰ with EA-IRMS and from 0 to 0.28‰ with GC-IRMS, indicating the high accuracy of the method. This newly established method measured the precision within 0.30‰ and was successfully validated to discriminate imported real wine samples with identical label but amazing price differences from different importers.

Fuzzy Evaluation Output of Taste Information for Liquor Using Electronic Tongue Based on Cloud Model

As a taste bionic system, electronic tongues can be used to derive taste information for different types of food. On this basis, we have carried forward the work by making it, in addition to the ability of accurately distinguish samples, be more expressive by speaking evaluative language like human beings. Thus, this paper demonstrates the correlation between the qualitative digital output of the taste bionic system and the fuzzy evaluation language that conform to the human perception mode. First, through principal component analysis (PCA), backward cloud generator and forward cloud generator, two-dimensional cloud droplet groups of different flavor information were established by using liquor taste data collected by electronic tongue. Second, the frequency and order of the evaluation words for different flavor of liquor were obtained by counting and analyzing the data appeared in the artificial sensory evaluation experiment. According to the frequency and order of words, the cloud droplet range corresponding to each word was calculated in the cloud drop group. Finally, the fuzzy evaluations that originated from the eight groups of liquor data with different flavor were compared with the artificial sense, and the results indicated that the model developed in this work is capable of outputting fuzzy evaluation that is consistent with human perception rather than digital output. To sum up, this method enabled the electronic tongue system to generate an output, which conforms to human's descriptive language, making food detection technology a step closer to human perception.

Voltammetric Electronic Tongue for the Simultaneous Determination of Three Benzodiazepines

The presented manuscript reports the simultaneous detection of a ternary mixture of the benzodiazepines diazepam, lorazepam, and flunitrazepam using an array of voltammetric sensors and the electronic tongue principle. The electrodes used in the array were selected from a set of differently modified graphite epoxy composite electrodes; specifically, six electrodes were used incorporating metallic nanoparticles of Cu and Pt, oxide nanoparticles of CuO and WO₃, plus pristine electrodes of epoxy-graphite and metallic Pt disk. Cyclic voltammetry was the technique used to obtain the voltammetric responses. Multivariate examination using Principal Component Analysis (PCA) justified the choice of sensors in order to get the proper discrimination of the benzodiazepines. Next, a quantitative model to predict the concentrations of mixtures of the three benzodiazepines was built employing the set of voltammograms, and was first processed with the Discrete Wavelet Transform, which fed an artificial neural network response model. The developed model successfully predicted the concentration of the three compounds with a normalized root mean square error (NRMSE) of 0.034 and 0.106 for the training and test subsets, respectively, and coefficient of correlation R ≥ 0.938 in the predicted vs. expected concentrations comparison graph.

Tuning the Electrical Properties of Electrospun Nanofibers with Hybrid Nanomaterials for Detecting Isoborneol in Water Using an Electronic Tongue

The presence of contaminants in water is a subject of paramount importance nowadays, which can make water improper to human consumption even when these contaminants are present at very low concentrations, causing health issues and economic losses. In this work, we evaluated the performance of nanocomposites based on nylon 6,6/chitosan electrospun nanofibers modified by cellulose nanowhiskers combined with functional materials like silver nanoparticles, gold nanoparticles, and reduced graphene oxide to be used as sensing layers of an electronic tongue (e-tongue) to detect Isoborneol. This compound, found in some plants and essential oils, is used as a natural repellent and also to produce many other chemicals. Additionally, its chemical structure is related to that of 2-methylisoborneol, a critical pollutant in aqueous media. The synergism between the nanomaterials combined with electrospun nanofibers could be verified by the enhancement of the charge transference ability. Additionally, electrical capacitance data measured with the impedimetric e-tongue were treated by Principal Component Analysis (PCA), and revealed the sensing system was able to discriminate samples contaminated with Isoborneol at nanomolar concentrations. Moreover, the electronic tongue system could detect Isoborneol in real water samples under different concentrations.

Analysis of musts and wines by means of a bio-electronic tongue based on tyrosinase and glucose oxidase using polypyrrole/gold nanoparticles as the electron mediator

A bioelectronic tongue (bioET) based on combinations of enzymes (tyrosinase and glucose oxidase) and polypyrrole (Ppy) or polypyrrole/AuNP (Ppy/AuNP) composites was build up and applied to the analysis and discrimination of musts and wines. Voltammetric responses of the array of sensors demonstrated the effectiveness of polymers as electron mediators and the existence of favorable synergistic effects between Ppy and the AuNPs. Using Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts according to the °Brix and TPI (Total Polyphenol Index), and wines according to the alcoholic degree and TPI. Partial Least Squares provided good correlations between the bioET output and traditional chemical parameters. Moreover, Support Vector Machines permitted to predict the TPI and the alcoholic degree of wines, from data provided by the bioET in the corresponding grapes. This result opens the possibility to predict wine characteristics from the beginning of the vinification process.

The qualitative and quantitative assessment of tea quality based on E-nose, E-tongue and E-eye combined with chemometrics

Electronic nose (E-nose), electronic tongue (E-tongue) and electronic eye (E-eye) combined with chemometrics methods were applied for qualitative identification and quantitative prediction of tea quality. Main chemical components, such as amino acids, catechins, polyphenols and caffeine were measured by traditional methods. Feature-level fusion strategy for the integration of the signals was introduced to integrate the E-nose, E-tongue and E-eye signals, aiming at improving the performances of identification and prediction models. Perfect results with an accuracy of 100% were obtained for qualitative identification of tea quality grades, based on fusion signals by support vector machine and random forest. Quantitative models were established for predicting the contents of the chemical components based on independent electronic signals and fusion signals by partial least squares regression, support vector machine and random forest. Random forest based on the fusion signals achieved the best performance in predicting the concentration of those chemical components.

Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection

Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.

Paper based electronic tongue - a low-cost solution for the distinction of sugar type and apple juice brand

This work reports on a low cost microfluidic electronic tongue (e-tongue) made with carbon interdigitated electrodes, printed on paper, and coated with boronic acid-containing hydrogels. Using capacitance measurements, the e-tongue was capable of distinguishing between different types of sugars (e.g. glucose, fructose and sucrose), in addition to distinguishing between commercial brands of apple juice using a small volume of sample (6 μL). The channels of the microfluidic e-tongue were made using a wax printer, and were modified with hydrogels containing acrylamide copolymerized with 5 or 20 mol% 3-(acrylamido) phenyl boronic acid (Am-PBA), or a crosslinked homopolymeric hydrogel based on N-(2-boronobenzyl)-2-hydroxy-N,N-dimethylethan-1-aminium-3-sulfopropyl acrylate (DMA-PBA). Such hydrogels, containing a phenyl boronic acid (PBA) moiety, can bind saccharides. Combining various hydrogels of this nature in an e-tongue device enabled discrimination between apple juices, which are known to contain higher amounts of fructose compared to glucose or sucrose. Changes in capacitance were captured with impedance spectroscopy in the frequency range from 0.1 to 10 MHz for solutions with varying concentrations of glucose, fructose and sucrose (from 0 to 0.056 g mL-1). The capacitance data were treated with Principal Component Analysis (PCA) and Interactive Document Map (IDMAP), which then correlated overall sugar content from different brands of apple juice. This low-cost, easy-to-use, disposable e-tongue offers great potential in the routine analysis of food and beverages, while offering comparative performance to alternatives in the literature.

A Future with Ubiquitous Sensing and Intelligent Systems

In this paper, we discuss the relevance of sensing and biosensing for the ongoing revolution in science and technology as a product of the merging of machine learning and Big Data into affordable technologies and accessible everyday products. Possible scenarios for the next decades are described with examples of intelligent systems for various areas, most of which will rely on ubiquitous sensing. The technological and societal challenges for developing the full potential of such intelligent systems are also addressed.

Monitoring the Phenolic Ripening of Red Grapes Using a Multisensor System Based on Metal-Oxide Nanoparticles

The maturity of grapes is usually monitored by means of the sugar concentration. However, the assessment of other parameters such as the phenolic content is also important because the phenolic maturity has an important impact on the organoleptic characteristics of wines. In this work, voltammetric sensors able to detect phenols in red grapes have been developed. They are based on metal oxide nanoparticles (CeO2, NiO, and TiO2,) whose excellent electrocatalytic properties toward phenols allows obtaining sensors with detection limits in the range of 10-8 M and coefficients of variation lower than 7%. An electronic tongue constructed using a combination of the nanoparticle-based sensors is capable to monitor the phenolic maturity of red grapes from véraison to maturity. Principal Component Analysis (PCA) can be successfully used to discriminate samples according to the ripeness. Regression models performed using Partial Least Squares (PLS-1) have established good correlations between voltammetric data obtained with the electrochemical sensors and the Total Polyphenolic Index, the Brix degree and the Total Acidity, with correlation coefficients close to 1 and low number of latent variables. An advantage of this system is that the electronic tongue can be used for the simultaneous assessment of these three parameters which are the main factors used to monitor the maturity of grapes. Thus the electronic tongue based on metal oxide nanoparticles can be a valuable tool to monitor ripeness. These results demonstrate the exciting possible applications of metal oxide nanoparticles in the field of electronic tongues.

The Taste of Commercially Available Clarithromycin Oral Pharmaceutical Suspensions in the Palestinian Market: Electronic Tongue and In Vivo Evaluation

Background: The taste of oral liquid dosage forms is a crucial factor that impacts paediatric patient compliance. The electronic tongue (ET) is an emerging tool that could be useful in taste assessment in order to minimize the involvement of humans in such evaluations. Purpose: The aim of this study is to evaluate the taste of commercially available clarithromycin (CM) oral pharmaceutical suspensions in the Palestinian market. Method: Commercially available CM suspensions (the brand Klacid^® and two generic K1 and K2) were assayed using the high performance liquid chromatography (HPLC) method. Then, the taste of these products was assessed using alpha-astree ET. In addition, an in vivo taste assessment was conducted on paediatric patients by a hedonic panel test. Moreover, volunteering community pharmacists were asked to rank the taste of these three products according to their experience from the best to the worst. Results: All suspension products had a CM concentration not less than 98% of the label amount. The ET results coupled with the principal component analysis (PCA) showed a very clear discrimination of the samples with different distances between groups (p-values < 0.001). Suspensions were in the following order in terms of taste: Klacid^® > K1 > K2. Moreover, The pattern discrimination index between (K1 and Klacid^®), (K1 and K2) and (Klacid^® and K2) were 8.81%, 65.75%, and 71.94%, respectively which suggests that K1 and Klacid^® are the most similar preparations in terms of taste. Interestingly, these results were in excellent agreement with the pharmacist ranking and patient acceptance test. Conclusions: The evaluated preparations showed significantly different taste within the order of Klacid^® > K1 > K2, as suggested by both the ET and in vivo results. Moreover, our results confirm the capability of alpha-astree ET in the taste assessment of oral suspensions and in predicting volunteer responses, which highlights its beneficial use as an in vitro taste assessment tool and as an alternative to human-based taste evaluations.

Electronic Tongue-A Tool for All Tastes?

Electronic tongue systems are traditionally used to analyse: food products, water samples and taste masking technologies for pharmaceuticals. In principle, their applications are almost limitless, as they are able to almost completely reduce the impact of interferents and can be applied to distinguish samples of extreme complexity as for example broths from different stages of fermentation. Nevertheless, their applications outside the three principal sample types are, in comparison, rather scarce. In this review, we would like to take a closer look on what are real capabilities of electronic tongue systems, what can be achieved using mixed sensor arrays and by introduction of biosensors or molecularly imprinted polymers in the matrix. We will discuss future directions both in the sense of applications as well as system development in the ever-growing trend of low cost analysis.

Portable Electronic Nose Based on Electrochemical Sensors for Food Quality Assessment

The steady increase in global consumption puts a strain on agriculture and might lead to a decrease in food quality. Currently used techniques of food analysis are often labour-intensive and time-consuming and require extensive sample preparation. For that reason, there is a demand for novel methods that could be used for rapid food quality assessment. A technique based on the use of an array of chemical sensors for holistic analysis of the sample’s headspace is called electronic olfaction. In this article, a prototype of a portable, modular electronic nose intended for food analysis is described. Using the SVM method, it was possible to classify samples of poultry meat based on shelf-life with 100% accuracy, and also samples of rapeseed oil based on the degree of thermal degradation with 100% accuracy. The prototype was also used to detect adulterations of extra virgin olive oil with rapeseed oil with 82% overall accuracy. Due to the modular design, the prototype offers the advantages of solutions targeted for analysis of specific food products, at the same time retaining the flexibility of application. Furthermore, its portability allows the device to be used at different stages of the production and distribution process.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively.

Elemental composition as a tool for the assessment of type, seasonal variability, and geographical origin of wine and its contribution to daily elemental intake

This study was designed to fill a gap, about Serbian wines, analysing 63 wines produced in 4 different regions in Serbia.